Support Antigravity SDK as a first-class agent implementation framework

### What is your feature suggestion?

lease add first-class support for agents implemented with the **Google Antigravity SDK for Python** (`google-antigravity`) in `agents-cli`.

Today, `agents-cli` already works well as a lifecycle toolchain for Google Cloud agent development, and it already documents support for Antigravity as a coding assistant that can use the CLI. This feature request is about a different layer: using **Antigravity SDK as the framework/runtime for the agent being created, run, evaluated, deployed, and published by `agents-cli`**.

A useful first version could be an Antigravity SDK project template:

```bash
agents-cli create my-antigravity-agent --agent antigravity-sdk-python
```

The generated project should include a minimal runnable Antigravity SDK agent and the surrounding `agents-cli` project structure needed for local development, deployment, evaluation, and documentation.

Desired capabilities, ideally delivered incrementally:

1. **Built-in Antigravity SDK template**
- Add an `antigravity-sdk-python` template alongside the existing agent templates.
- Generate a minimal but production-shaped Python project using `google-antigravity`.
- Include a runnable `Agent` / `LocalAgentConfig` example.
- Include examples for a custom Python tool and an optional MCP server configuration.

2. **Manifest support**
- Allow `agents-cli-manifest.yaml` to identify the agent framework as Antigravity SDK.
- Capture the Antigravity agent entrypoint, runtime/deployment target, environment variables, and supported lifecycle commands.

3. **Local run and playground support**
- Support running the generated Antigravity SDK agent locally through `agents-cli run` or document the supported local runner contract.
- Support local development with either a Gemini API key or Vertex / Gemini Enterprise Agent Platform configuration where applicable.

4. **Evaluation support**
- Support `agents-cli eval` for Antigravity SDK agents directly, or document an adapter interface that normalizes Antigravity SDK responses into the existing `agents-cli` evaluation format.
- If Antigravity SDK exposes streaming steps, tool calls, or traces, document how much of that information can be collected for evaluation.

5. **Deployment and publishing guidance**
- Document and test at least one supported deployment target, such as Cloud Run, Agent Runtime, or GKE.
- Clarify whether Antigravity SDK agents can be published to Gemini Enterprise Agent Platform through `agents-cli publish gemini-enterprise`.
- Provide safe defaults for service accounts, environment variables, secrets, ADC, and MCP configuration.

6. **Safety and governance examples**
- Include examples for Antigravity SDK hooks or policies, such as read-only tools, deny-by-default tool policy, or explicit approval before risky tool execution.
- Document recommended defaults for enterprise projects that use SaaS, public-cloud, or MCP tools.

### What will this enable you to do?

This would allow teams to use `agents-cli` as the standard Google Cloud lifecycle toolchain while choosing Antigravity SDK as the agent implementation framework.

Example workflows this would enable:

```bash
agents-cli create incident-triage-agent --agent antigravity --deployment-target cloud_run
agents-cli run "Summarize this incident and propose next actions"
agents-cli eval run
agents-cli deploy
agents-cli publish gemini-enterprise
```

More specifically, this would enable developers to:

1. **Build Antigravity SDK agents with production scaffolding**
- Start from a supported project layout instead of hand-rolling the integration.
- Use the same `agents-cli` lifecycle conventions as other Google Cloud agent projects.

2. **Use Antigravity SDK capabilities in enterprise agents**
- Implement agents with Antigravity SDK primitives.
- Use stateful conversations and streaming responses.
- Register custom Python tools.
- Integrate MCP servers as tool providers.
- Apply hooks and policies for safer tool execution.
- Configure local Gemini API key development and Google Cloud / Vertex / Gemini Enterprise modes where supported.

3. **Reduce framework fragmentation**
- Avoid forcing teams to choose between ADK-oriented `agents-cli` lifecycle support and Antigravity SDK-specific agent capabilities.
- Make it clear when to choose ADK, Antigravity SDK, or another framework for a given agent project.

4. **Improve enterprise governance**
- Give platform teams a consistent way to scaffold, evaluate, deploy, and review Antigravity SDK agents.
- Encourage safer defaults around credentials, service accounts, MCP tools, and production deployment.

5. **Support phased adoption**
- Teams could begin with a supported Antigravity SDK template and later adopt deeper `agents-cli` integration as run/eval/deploy/publish support matures.

### Additional context

I think this is worthwhile because `agents-cli` is positioned as the lifecycle CLI for building, evaluating, deploying, publishing, governing, and optimizing agents on Google Cloud, while Antigravity SDK is a Google Python SDK for building agents powered by Antigravity and Gemini. Developers may reasonably expect these two Google agent-development surfaces to work together not only at the coding-assistant layer, but also at the agent-implementation-framework layer.

Current context that motivated this request:

- `agents-cli` already documents support for Antigravity as a coding assistant that can use the CLI.
- The currently documented built-in templates appear to be ADK-oriented, such as `adk`, `adk_a2a`, and `agentic_rag`.
- `agents-cli create --agent` already supports template identifiers, local paths, ADK sample shortcuts, and remote Git URLs, which suggests a low-risk incremental path: start with a documented remote Antigravity SDK starter template, then promote it to a first-class built-in template if it proves useful.
- Antigravity SDK has its own agent API/runtime surface, including agent configuration, conversations, streaming, custom tools, MCP integration, hooks/policies, triggers, and Google Cloud / Vertex / Gemini Enterprise configuration.

Beginne mit der Überprüfung der vorhandenen --agent-Vorlagenbezeichner und der im Issue beschriebenen create-, run-, eval-, deploy- und publish-Einstiegspunkte. Definiere die kleinste unterstützte Antigravity SDK-Integration und überprüfe anschließend, dass deren Template, Manifest, Lifecycle-Verhalten, Deployment-Ziel und Evaluationsgrenze dokumentiert und getestet sind.

Vom Indexierungsmodell aus dem Issue-Text verfasst.